Overview

Dataset statistics

Number of variables22
Number of observations41176
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory176.0 B

Variable types

Numeric11
Categorical10
Boolean1

Alerts

df_index is highly correlated with emp.var.rate and 3 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
emp.var.rate is highly correlated with df_index and 3 other fieldsHigh correlation
cons.price.idx is highly correlated with df_index and 1 other fieldsHigh correlation
euribor3m is highly correlated with df_index and 2 other fieldsHigh correlation
nr.employed is highly correlated with df_index and 2 other fieldsHigh correlation
df_index is highly correlated with emp.var.rate and 3 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdays and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with df_index and 3 other fieldsHigh correlation
cons.price.idx is highly correlated with df_index and 3 other fieldsHigh correlation
euribor3m is highly correlated with df_index and 3 other fieldsHigh correlation
nr.employed is highly correlated with df_index and 4 other fieldsHigh correlation
df_index is highly correlated with cons.price.idxHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with df_index and 1 other fieldsHigh correlation
euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
nr.employed is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
housing is highly correlated with loanHigh correlation
loan is highly correlated with housingHigh correlation
month is highly correlated with contactHigh correlation
contact is highly correlated with monthHigh correlation
df_index is highly correlated with contact and 9 other fieldsHigh correlation
age is highly correlated with jobHigh correlation
job is highly correlated with age and 1 other fieldsHigh correlation
education is highly correlated with jobHigh correlation
housing is highly correlated with loanHigh correlation
loan is highly correlated with housingHigh correlation
contact is highly correlated with df_index and 6 other fieldsHigh correlation
month is highly correlated with df_index and 6 other fieldsHigh correlation
pdays is highly correlated with df_index and 5 other fieldsHigh correlation
previous is highly correlated with pdays and 2 other fieldsHigh correlation
poutcome is highly correlated with df_index and 7 other fieldsHigh correlation
emp.var.rate is highly correlated with df_index and 7 other fieldsHigh correlation
cons.price.idx is highly correlated with df_index and 7 other fieldsHigh correlation
cons.conf.idx is highly correlated with df_index and 9 other fieldsHigh correlation
euribor3m is highly correlated with df_index and 10 other fieldsHigh correlation
nr.employed is highly correlated with df_index and 9 other fieldsHigh correlation
output is highly correlated with df_index and 3 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
previous has 35551 (86.3%) zeros Zeros

Reproduction

Analysis started2022-06-20 13:41:59.527402
Analysis finished2022-06-20 13:42:34.036175
Duration34.51 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct41176
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20593.05673
Minimum0
Maximum41187
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2022-06-20T21:42:34.195750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2059.75
Q110294.75
median20594.5
Q330890.25
95-th percentile39128.25
Maximum41187
Range41187
Interquartile range (IQR)20595.5

Descriptive statistics

Standard deviation11890.49312
Coefficient of variation (CV)0.5774030187
Kurtosis-1.200129397
Mean20593.05673
Median Absolute Deviation (MAD)10298
Skewness5.289244425 × 10-5
Sum847939704
Variance141383826.7
MonotonicityStrictly increasing
2022-06-20T21:42:34.399205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
275121
 
< 0.1%
274541
 
< 0.1%
274551
 
< 0.1%
274561
 
< 0.1%
274571
 
< 0.1%
274581
 
< 0.1%
274591
 
< 0.1%
274601
 
< 0.1%
274611
 
< 0.1%
Other values (41166)41166
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
411871
< 0.1%
411861
< 0.1%
411851
< 0.1%
411841
< 0.1%
411831
< 0.1%
411821
< 0.1%
411811
< 0.1%
411801
< 0.1%
411791
< 0.1%
411781
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.02380027
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2022-06-20T21:42:34.614629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.42067987
Coefficient of variation (CV)0.2603620795
Kurtosis0.7911133226
Mean40.02380027
Median Absolute Deviation (MAD)7
Skewness0.7845602604
Sum1648020
Variance108.5905689
MonotonicityNot monotonic
2022-06-20T21:42:34.832049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311947
 
4.7%
321845
 
4.5%
331833
 
4.5%
361779
 
4.3%
351758
 
4.3%
341745
 
4.2%
301714
 
4.2%
371475
 
3.6%
291453
 
3.5%
391430
 
3.5%
Other values (68)24197
58.8%
ValueCountFrequency (%)
175
 
< 0.1%
1828
 
0.1%
1942
 
0.1%
2065
 
0.2%
21102
 
0.2%
22137
 
0.3%
23226
 
0.5%
24462
1.1%
25598
1.5%
26698
1.7%
ValueCountFrequency (%)
982
 
< 0.1%
951
 
< 0.1%
941
 
< 0.1%
924
 
< 0.1%
912
 
< 0.1%
892
 
< 0.1%
8822
0.1%
871
 
< 0.1%
868
 
< 0.1%
8515
< 0.1%

job
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
admin
10419 
blue-collar
9253 
technician
6739 
services
3967 
management
2924 
Other values (7)
7874 

Length

Max length13
Median length12
Mean length8.702399456
Min length5

Characters and Unicode

Total characters358330
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousemaid
2nd rowservices
3rd rowservices
4th rowadmin
5th rowservices

Common Values

ValueCountFrequency (%)
admin10419
25.3%
blue-collar9253
22.5%
technician6739
16.4%
services3967
 
9.6%
management2924
 
7.1%
retired1718
 
4.2%
entrepreneur1456
 
3.5%
self-employed1421
 
3.5%
housemaid1060
 
2.6%
unemployed1014
 
2.5%
Other values (2)1205
 
2.9%

Length

2022-06-20T21:42:35.025532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin10419
25.3%
blue-collar9253
22.5%
technician6739
16.4%
services3967
 
9.6%
management2924
 
7.1%
retired1718
 
4.2%
entrepreneur1456
 
3.5%
self-employed1421
 
3.5%
housemaid1060
 
2.6%
unemployed1014
 
2.5%
Other values (2)1205
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e47260
13.2%
n35536
9.9%
a33319
9.3%
l31615
 
8.8%
i30642
 
8.6%
c26698
 
7.5%
r21024
 
5.9%
m19762
 
5.5%
d16507
 
4.6%
t14587
 
4.1%
Other values (13)81380
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter347656
97.0%
Dash Punctuation10674
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e47260
13.6%
n35536
10.2%
a33319
9.6%
l31615
9.1%
i30642
8.8%
c26698
 
7.7%
r21024
 
6.0%
m19762
 
5.7%
d16507
 
4.7%
t14587
 
4.2%
Other values (12)70706
20.3%
Dash Punctuation
ValueCountFrequency (%)
-10674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin347656
97.0%
Common10674
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e47260
13.6%
n35536
10.2%
a33319
9.6%
l31615
9.1%
i30642
8.8%
c26698
 
7.7%
r21024
 
6.0%
m19762
 
5.7%
d16507
 
4.7%
t14587
 
4.2%
Other values (12)70706
20.3%
Common
ValueCountFrequency (%)
-10674
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII358330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e47260
13.2%
n35536
9.9%
a33319
9.3%
l31615
 
8.8%
i30642
 
8.6%
c26698
 
7.5%
r21024
 
5.9%
m19762
 
5.5%
d16507
 
4.6%
t14587
 
4.1%
Other values (13)81380
22.7%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
married
24921 
single
11564 
divorced
4611 
unknown
 
80

Length

Max length8
Median length7
Mean length6.831139499
Min length6

Characters and Unicode

Total characters281279
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married24921
60.5%
single11564
28.1%
divorced4611
 
11.2%
unknown80
 
0.2%

Length

2022-06-20T21:42:35.199071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-20T21:42:35.390558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
married24921
60.5%
single11564
28.1%
divorced4611
 
11.2%
unknown80
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r54453
19.4%
i41096
14.6%
e41096
14.6%
d34143
12.1%
m24921
8.9%
a24921
8.9%
n11804
 
4.2%
s11564
 
4.1%
g11564
 
4.1%
l11564
 
4.1%
Other values (6)14153
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter281279
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r54453
19.4%
i41096
14.6%
e41096
14.6%
d34143
12.1%
m24921
8.9%
a24921
8.9%
n11804
 
4.2%
s11564
 
4.1%
g11564
 
4.1%
l11564
 
4.1%
Other values (6)14153
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin281279
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r54453
19.4%
i41096
14.6%
e41096
14.6%
d34143
12.1%
m24921
8.9%
a24921
8.9%
n11804
 
4.2%
s11564
 
4.1%
g11564
 
4.1%
l11564
 
4.1%
Other values (6)14153
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII281279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r54453
19.4%
i41096
14.6%
e41096
14.6%
d34143
12.1%
m24921
8.9%
a24921
8.9%
n11804
 
4.2%
s11564
 
4.1%
g11564
 
4.1%
l11564
 
4.1%
Other values (6)14153
 
5.0%

education
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
university.degree
12164 
high.school
9512 
basic.9y
6045 
professional.course
5240 
basic.4y
4176 
Other values (3)
4039 

Length

Max length19
Median length17
Mean length12.71046241
Min length7

Characters and Unicode

Total characters523366
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.4y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.6y
5th rowhigh.school

Common Values

ValueCountFrequency (%)
university.degree12164
29.5%
high.school9512
23.1%
basic.9y6045
14.7%
professional.course5240
12.7%
basic.4y4176
 
10.1%
basic.6y2291
 
5.6%
unknown1730
 
4.2%
illiterate18
 
< 0.1%

Length

2022-06-20T21:42:35.568082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-20T21:42:35.782508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
university.degree12164
29.5%
high.school9512
23.1%
basic.9y6045
14.7%
professional.course5240
12.7%
basic.4y4176
 
10.1%
basic.6y2291
 
5.6%
unknown1730
 
4.2%
illiterate18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e59172
 
11.3%
i51628
 
9.9%
s49908
 
9.5%
.39428
 
7.5%
o36474
 
7.0%
r34826
 
6.7%
h28536
 
5.5%
c27264
 
5.2%
y24676
 
4.7%
n22594
 
4.3%
Other values (15)148860
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter471426
90.1%
Other Punctuation39428
 
7.5%
Decimal Number12512
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e59172
12.6%
i51628
11.0%
s49908
10.6%
o36474
 
7.7%
r34826
 
7.4%
h28536
 
6.1%
c27264
 
5.8%
y24676
 
5.2%
n22594
 
4.8%
g21676
 
4.6%
Other values (11)114672
24.3%
Decimal Number
ValueCountFrequency (%)
96045
48.3%
44176
33.4%
62291
 
18.3%
Other Punctuation
ValueCountFrequency (%)
.39428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin471426
90.1%
Common51940
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e59172
12.6%
i51628
11.0%
s49908
10.6%
o36474
 
7.7%
r34826
 
7.4%
h28536
 
6.1%
c27264
 
5.8%
y24676
 
5.2%
n22594
 
4.8%
g21676
 
4.6%
Other values (11)114672
24.3%
Common
ValueCountFrequency (%)
.39428
75.9%
96045
 
11.6%
44176
 
8.0%
62291
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII523366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e59172
 
11.3%
i51628
 
9.9%
s49908
 
9.5%
.39428
 
7.5%
o36474
 
7.0%
r34826
 
6.7%
h28536
 
5.5%
c27264
 
5.2%
y24676
 
4.7%
n22594
 
4.3%
Other values (15)148860
28.4%

default
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
no
32577 
unknown
8596 
yes
 
3

Length

Max length7
Median length2
Mean length3.043884787
Min length2

Characters and Unicode

Total characters125335
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowunknown
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no32577
79.1%
unknown8596
 
20.9%
yes3
 
< 0.1%

Length

2022-06-20T21:42:35.986963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-20T21:42:36.153518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no32577
79.1%
unknown8596
 
20.9%
yes3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n58365
46.6%
o41173
32.9%
u8596
 
6.9%
k8596
 
6.9%
w8596
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter125335
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n58365
46.6%
o41173
32.9%
u8596
 
6.9%
k8596
 
6.9%
w8596
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin125335
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n58365
46.6%
o41173
32.9%
u8596
 
6.9%
k8596
 
6.9%
w8596
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n58365
46.6%
o41173
32.9%
u8596
 
6.9%
k8596
 
6.9%
w8596
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

housing
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
yes
21571 
no
18615 
unknown
 
990

Length

Max length7
Median length3
Mean length2.64408879
Min length2

Characters and Unicode

Total characters108873
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
yes21571
52.4%
no18615
45.2%
unknown990
 
2.4%

Length

2022-06-20T21:42:36.317080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-20T21:42:36.484635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
yes21571
52.4%
no18615
45.2%
unknown990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n21585
19.8%
y21571
19.8%
e21571
19.8%
s21571
19.8%
o19605
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter108873
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n21585
19.8%
y21571
19.8%
e21571
19.8%
s21571
19.8%
o19605
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin108873
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n21585
19.8%
y21571
19.8%
e21571
19.8%
s21571
19.8%
o19605
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII108873
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n21585
19.8%
y21571
19.8%
e21571
19.8%
s21571
19.8%
o19605
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

loan
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
no
33938 
yes
6248 
unknown
 
990

Length

Max length7
Median length2
Mean length2.271954537
Min length2

Characters and Unicode

Total characters93550
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no33938
82.4%
yes6248
 
15.2%
unknown990
 
2.4%

Length

2022-06-20T21:42:36.650193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-20T21:42:36.841678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no33938
82.4%
yes6248
 
15.2%
unknown990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n36908
39.5%
o34928
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter93550
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n36908
39.5%
o34928
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin93550
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n36908
39.5%
o34928
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII93550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n36908
39.5%
o34928
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

contact
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
cellular
26135 
telephone
15041 

Length

Max length9
Median length8
Mean length8.365285603
Min length8

Characters and Unicode

Total characters344449
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular26135
63.5%
telephone15041
36.5%

Length

2022-06-20T21:42:36.985295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-20T21:42:37.156836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
cellular26135
63.5%
telephone15041
36.5%

Most occurring characters

ValueCountFrequency (%)
l93446
27.1%
e71258
20.7%
c26135
 
7.6%
u26135
 
7.6%
a26135
 
7.6%
r26135
 
7.6%
t15041
 
4.4%
p15041
 
4.4%
h15041
 
4.4%
o15041
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter344449
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l93446
27.1%
e71258
20.7%
c26135
 
7.6%
u26135
 
7.6%
a26135
 
7.6%
r26135
 
7.6%
t15041
 
4.4%
p15041
 
4.4%
h15041
 
4.4%
o15041
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin344449
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l93446
27.1%
e71258
20.7%
c26135
 
7.6%
u26135
 
7.6%
a26135
 
7.6%
r26135
 
7.6%
t15041
 
4.4%
p15041
 
4.4%
h15041
 
4.4%
o15041
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII344449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l93446
27.1%
e71258
20.7%
c26135
 
7.6%
u26135
 
7.6%
a26135
 
7.6%
r26135
 
7.6%
t15041
 
4.4%
p15041
 
4.4%
h15041
 
4.4%
o15041
 
4.4%

month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
may
13767 
jul
7169 
aug
6176 
jun
5318 
nov
4100 
Other values (5)
4646 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123528
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may13767
33.4%
jul7169
17.4%
aug6176
15.0%
jun5318
 
12.9%
nov4100
 
10.0%
apr2631
 
6.4%
oct717
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Length

2022-06-20T21:42:37.302447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-20T21:42:37.515878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
may13767
33.4%
jul7169
17.4%
aug6176
15.0%
jun5318
 
12.9%
nov4100
 
10.0%
apr2631
 
6.4%
oct717
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a23120
18.7%
u18663
15.1%
m14313
11.6%
y13767
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6176
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter123528
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a23120
18.7%
u18663
15.1%
m14313
11.6%
y13767
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6176
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin123528
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a23120
18.7%
u18663
15.1%
m14313
11.6%
y13767
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6176
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII123528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a23120
18.7%
u18663
15.1%
m14313
11.6%
y13767
11.1%
j12487
10.1%
n9418
7.6%
l7169
 
5.8%
g6176
 
5.0%
o4817
 
3.9%
v4100
 
3.3%
Other values (7)9498
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
thu
8618 
mon
8512 
wed
8134 
tue
8086 
fri
7826 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123528
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu8618
20.9%
mon8512
20.7%
wed8134
19.8%
tue8086
19.6%
fri7826
19.0%

Length

2022-06-20T21:42:37.717338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-20T21:42:37.899850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
thu8618
20.9%
mon8512
20.7%
wed8134
19.8%
tue8086
19.6%
fri7826
19.0%

Most occurring characters

ValueCountFrequency (%)
t16704
13.5%
u16704
13.5%
e16220
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8134
6.6%
d8134
6.6%
f7826
6.3%
Other values (2)15652
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter123528
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t16704
13.5%
u16704
13.5%
e16220
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8134
6.6%
d8134
6.6%
f7826
6.3%
Other values (2)15652
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin123528
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t16704
13.5%
u16704
13.5%
e16220
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8134
6.6%
d8134
6.6%
f7826
6.3%
Other values (2)15652
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII123528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t16704
13.5%
u16704
13.5%
e16220
13.1%
h8618
7.0%
m8512
6.9%
o8512
6.9%
n8512
6.9%
w8134
6.6%
d8134
6.6%
f7826
6.3%
Other values (2)15652
12.7%

duration
Real number (ℝ≥0)

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.315815
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2022-06-20T21:42:38.117269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile753
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.305321
Coefficient of variation (CV)1.003830605
Kurtosis20.24377094
Mean258.315815
Median Absolute Deviation (MAD)94
Skewness3.262807509
Sum10636412
Variance67239.24948
MonotonicityNot monotonic
2022-06-20T21:42:38.323717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90170
 
0.4%
85170
 
0.4%
136168
 
0.4%
73167
 
0.4%
124163
 
0.4%
87162
 
0.4%
72161
 
0.4%
104161
 
0.4%
111160
 
0.4%
106159
 
0.4%
Other values (1534)39535
96.0%
ValueCountFrequency (%)
04
 
< 0.1%
13
 
< 0.1%
21
 
< 0.1%
33
 
< 0.1%
412
 
< 0.1%
530
 
0.1%
637
0.1%
754
0.1%
869
0.2%
977
0.2%
ValueCountFrequency (%)
49181
< 0.1%
41991
< 0.1%
37851
< 0.1%
36431
< 0.1%
36311
< 0.1%
35091
< 0.1%
34221
< 0.1%
33661
< 0.1%
33221
< 0.1%
32841
< 0.1%

campaign
Real number (ℝ≥0)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.567879347
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2022-06-20T21:42:38.525179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.770318336
Coefficient of variation (CV)1.078835086
Kurtosis36.9718574
Mean2.567879347
Median Absolute Deviation (MAD)1
Skewness4.762044061
Sum105735
Variance7.674663685
MonotonicityNot monotonic
2022-06-20T21:42:38.691733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
117634
42.8%
210568
25.7%
35340
 
13.0%
42650
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
Other values (32)869
 
2.1%
ValueCountFrequency (%)
117634
42.8%
210568
25.7%
35340
 
13.0%
42650
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
ValueCountFrequency (%)
561
 
< 0.1%
432
 
< 0.1%
422
 
< 0.1%
411
 
< 0.1%
402
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
355
< 0.1%
343
< 0.1%
334
< 0.1%

pdays
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.4648096
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2022-06-20T21:42:38.872252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.9371017
Coefficient of variation (CV)0.1942274667
Kurtosis22.22155279
Mean962.4648096
Median Absolute Deviation (MAD)0
Skewness-4.921386382
Sum39630451
Variance34945.48
MonotonicityNot monotonic
2022-06-20T21:42:39.060747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
99939661
96.3%
3439
 
1.1%
6412
 
1.0%
4118
 
0.3%
964
 
0.2%
261
 
0.1%
760
 
0.1%
1258
 
0.1%
1052
 
0.1%
546
 
0.1%
Other values (17)205
 
0.5%
ValueCountFrequency (%)
015
 
< 0.1%
126
 
0.1%
261
 
0.1%
3439
1.1%
4118
 
0.3%
546
 
0.1%
6412
1.0%
760
 
0.1%
818
 
< 0.1%
964
 
0.2%
ValueCountFrequency (%)
99939661
96.3%
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
223
 
< 0.1%
212
 
< 0.1%
201
 
< 0.1%
193
 
< 0.1%
187
 
< 0.1%
178
 
< 0.1%

previous
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1730134059
Minimum0
Maximum7
Zeros35551
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2022-06-20T21:42:39.211345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4949643814
Coefficient of variation (CV)2.8608441
Kurtosis20.10216376
Mean0.1730134059
Median Absolute Deviation (MAD)0
Skewness3.831395514
Sum7124
Variance0.2449897388
MonotonicityNot monotonic
2022-06-20T21:42:39.345988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
035551
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
035551
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
65
 
< 0.1%
518
 
< 0.1%
470
 
0.2%
3216
 
0.5%
2754
 
1.8%
14561
 
11.1%
035551
86.3%

poutcome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.8 KiB
nonexistent
35551 
failure
4252 
success
 
1373

Length

Max length11
Median length11
Mean length10.45356518
Min length7

Characters and Unicode

Total characters430436
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent35551
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Length

2022-06-20T21:42:39.751900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-20T21:42:39.913470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent35551
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n106653
24.8%
e76727
17.8%
t71102
16.5%
i39803
 
9.2%
s39670
 
9.2%
o35551
 
8.3%
x35551
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter430436
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n106653
24.8%
e76727
17.8%
t71102
16.5%
i39803
 
9.2%
s39670
 
9.2%
o35551
 
8.3%
x35551
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin430436
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n106653
24.8%
e76727
17.8%
t71102
16.5%
i39803
 
9.2%
s39670
 
9.2%
o35551
 
8.3%
x35551
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII430436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n106653
24.8%
e76727
17.8%
t71102
16.5%
i39803
 
9.2%
s39670
 
9.2%
o35551
 
8.3%
x35551
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08192150767
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17186
Negative (%)41.7%
Memory size321.8 KiB
2022-06-20T21:42:40.061075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.570882615
Coefficient of variation (CV)19.17546026
Kurtosis-1.062698024
Mean0.08192150767
Median Absolute Deviation (MAD)0.3
Skewness-0.7240605917
Sum3373.2
Variance2.467672189
MonotonicityNot monotonic
2022-06-20T21:42:40.202697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.416228
39.4%
-1.89182
22.3%
1.17762
18.9%
-0.13682
 
8.9%
-2.91662
 
4.0%
-3.41070
 
2.6%
-1.7773
 
1.9%
-1.1635
 
1.5%
-3172
 
0.4%
-0.210
 
< 0.1%
ValueCountFrequency (%)
-3.41070
 
2.6%
-3172
 
0.4%
-2.91662
 
4.0%
-1.89182
22.3%
-1.7773
 
1.9%
-1.1635
 
1.5%
-0.210
 
< 0.1%
-0.13682
 
8.9%
1.17762
18.9%
1.416228
39.4%
ValueCountFrequency (%)
1.416228
39.4%
1.17762
18.9%
-0.13682
 
8.9%
-0.210
 
< 0.1%
-1.1635
 
1.5%
-1.7773
 
1.9%
-1.89182
22.3%
-2.91662
 
4.0%
-3172
 
0.4%
-3.41070
 
2.6%

cons.price.idx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57571989
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2022-06-20T21:42:40.350302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.5788389856
Coefficient of variation (CV)0.006185781806
Kurtosis-0.8298510691
Mean93.57571989
Median Absolute Deviation (MAD)0.38
Skewness-0.2308529068
Sum3853073.842
Variance0.3350545712
MonotonicityNot monotonic
2022-06-20T21:42:40.531817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9947762
18.9%
93.9186681
16.2%
92.8935793
14.1%
93.4445173
12.6%
94.4654374
10.6%
93.23615
8.8%
93.0752457
 
6.0%
92.201770
 
1.9%
92.963715
 
1.7%
92.431446
 
1.1%
Other values (16)3390
8.2%
ValueCountFrequency (%)
92.201770
 
1.9%
92.379267
 
0.6%
92.431446
 
1.1%
92.469177
 
0.4%
92.649357
 
0.9%
92.713172
 
0.4%
92.75610
 
< 0.1%
92.843282
 
0.7%
92.8935793
14.1%
92.963715
 
1.7%
ValueCountFrequency (%)
94.767128
 
0.3%
94.601204
 
0.5%
94.4654374
10.6%
94.215311
 
0.8%
94.199303
 
0.7%
94.055229
 
0.6%
94.027233
 
0.6%
93.9947762
18.9%
93.9186681
16.2%
93.876212
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.50286332
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41176
Negative (%)100.0%
Memory size321.8 KiB
2022-06-20T21:42:40.688398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.627859965
Coefficient of variation (CV)-0.1142600692
Kurtosis-0.3590970525
Mean-40.50286332
Median Absolute Deviation (MAD)4.4
Skewness0.3028760001
Sum-1667745.9
Variance21.41708785
MonotonicityNot monotonic
2022-06-20T21:42:40.898836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.47762
18.9%
-42.76681
16.2%
-46.25793
14.1%
-36.15173
12.6%
-41.84374
10.6%
-423615
8.8%
-47.12457
 
6.0%
-31.4770
 
1.9%
-40.8715
 
1.7%
-26.9446
 
1.1%
Other values (16)3390
8.2%
ValueCountFrequency (%)
-50.8128
 
0.3%
-50282
 
0.7%
-49.5204
 
0.5%
-47.12457
 
6.0%
-46.25793
14.1%
-45.910
 
< 0.1%
-42.76681
16.2%
-423615
8.8%
-41.84374
10.6%
-40.8715
 
1.7%
ValueCountFrequency (%)
-26.9446
 
1.1%
-29.8267
 
0.6%
-30.1357
 
0.9%
-31.4770
 
1.9%
-33172
 
0.4%
-33.6177
 
0.4%
-34.6174
 
0.4%
-34.8264
 
0.6%
-36.15173
12.6%
-36.47762
18.9%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.621293448
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2022-06-20T21:42:41.201029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.734437004
Coefficient of variation (CV)0.4789551106
Kurtosis-1.40679132
Mean3.621293448
Median Absolute Deviation (MAD)0.108
Skewness-0.7091942126
Sum149110.379
Variance3.00827172
MonotonicityNot monotonic
2022-06-20T21:42:41.460335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8572868
 
7.0%
4.9622611
 
6.3%
4.9632487
 
6.0%
4.9611902
 
4.6%
4.8561210
 
2.9%
4.9641175
 
2.9%
1.4051169
 
2.8%
4.9651070
 
2.6%
4.8641044
 
2.5%
4.961013
 
2.5%
Other values (306)24627
59.8%
ValueCountFrequency (%)
0.6348
 
< 0.1%
0.63543
0.1%
0.63614
 
< 0.1%
0.6376
 
< 0.1%
0.6387
 
< 0.1%
0.63916
 
< 0.1%
0.6410
 
< 0.1%
0.64235
0.1%
0.64323
0.1%
0.64438
0.1%
ValueCountFrequency (%)
5.0459
 
< 0.1%
57
 
< 0.1%
4.97172
 
0.4%
4.968991
 
2.4%
4.967643
 
1.6%
4.966620
 
1.5%
4.9651070
2.6%
4.9641175
2.9%
4.9632487
6.0%
4.9622611
6.3%

nr.employed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.03487
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.8 KiB
2022-06-20T21:42:41.613924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.25136397
Coefficient of variation (CV)0.01398313845
Kurtosis-0.003539670085
Mean5167.03487
Median Absolute Deviation (MAD)37.1
Skewness-1.044317057
Sum212757827.8
Variance5220.259596
MonotonicityNot monotonic
2022-06-20T21:42:41.748564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.116228
39.4%
5099.18532
20.7%
51917762
18.9%
5195.83682
 
8.9%
5076.21662
 
4.0%
5017.51070
 
2.6%
4991.6773
 
1.9%
5008.7650
 
1.6%
4963.6635
 
1.5%
5023.5172
 
0.4%
ValueCountFrequency (%)
4963.6635
 
1.5%
4991.6773
 
1.9%
5008.7650
 
1.6%
5017.51070
 
2.6%
5023.5172
 
0.4%
5076.21662
 
4.0%
5099.18532
20.7%
5176.310
 
< 0.1%
51917762
18.9%
5195.83682
8.9%
ValueCountFrequency (%)
5228.116228
39.4%
5195.83682
 
8.9%
51917762
18.9%
5176.310
 
< 0.1%
5099.18532
20.7%
5076.21662
 
4.0%
5023.5172
 
0.4%
5017.51070
 
2.6%
5008.7650
 
1.6%
4991.6773
 
1.9%

output
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
False
36537 
True
4639 
ValueCountFrequency (%)
False36537
88.7%
True4639
 
11.3%
2022-06-20T21:42:41.912126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2022-06-20T21:42:30.087727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:06.136737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:09.477809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:12.473802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:15.058893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:17.209148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:19.389320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:21.790900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:23.885306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:26.002647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:28.057155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:30.457738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:06.369116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:09.744097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:12.651327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:15.292271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:17.377696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:19.588787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:21.994359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:24.114692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:26.174187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:28.225706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:30.666184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:06.609474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:10.081196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:12.871739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:15.512681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:17.607083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:19.760329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:22.198811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:24.314159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:26.385622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:28.416196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:30.851688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:07.431279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:10.356462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:13.075195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:15.716137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:17.791589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:19.962788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:22.391298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:24.495672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:26.586086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:28.609679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:31.050157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:07.673630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:10.621752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:13.290619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:15.920591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:18.006019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:20.146299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:22.580791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:24.667215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:26.765607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:28.802164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:31.229676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:07.915983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:10.909982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:13.502054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:16.118063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:18.198502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:20.351751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:22.759315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:24.834768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:26.956100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:28.997642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:31.415183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:08.194239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:11.189235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:13.726454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:16.303566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:18.448833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:20.602079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:22.967757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:25.014288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:27.153569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:29.181151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:31.599689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:08.444571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:11.434579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:13.954843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:16.483088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:18.625363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:20.831465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:23.145293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:25.184830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:27.325113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:29.366657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:31.798157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:08.719835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:11.688899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:14.202182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:16.666596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:18.813858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:21.041903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:23.334776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:25.416214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:27.507624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:29.561136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:31.981668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:08.986123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:11.960174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:14.592140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:16.853097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:19.003352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:21.220427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:23.528259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:25.636624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:27.690135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:29.746643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:32.177145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:09.225484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:12.230451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:14.827513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:17.035612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:19.188856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:21.431862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:23.709773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:25.819137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:27.867661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-20T21:42:29.907212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-06-20T21:42:42.048762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-20T21:42:42.299094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-20T21:42:42.545435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-20T21:42:42.773825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-20T21:42:43.040113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-20T21:42:32.745625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-20T21:42:33.642229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedoutput
0056housemaidmarriedbasic.4ynononotelephonemaymon26119990nonexistent1.193.994-36.44.8575191.0no
1157servicesmarriedhigh.schoolunknownnonotelephonemaymon14919990nonexistent1.193.994-36.44.8575191.0no
2237servicesmarriedhigh.schoolnoyesnotelephonemaymon22619990nonexistent1.193.994-36.44.8575191.0no
3340adminmarriedbasic.6ynononotelephonemaymon15119990nonexistent1.193.994-36.44.8575191.0no
4456servicesmarriedhigh.schoolnonoyestelephonemaymon30719990nonexistent1.193.994-36.44.8575191.0no
5545servicesmarriedbasic.9yunknownnonotelephonemaymon19819990nonexistent1.193.994-36.44.8575191.0no
6659adminmarriedprofessional.coursenononotelephonemaymon13919990nonexistent1.193.994-36.44.8575191.0no
7741blue-collarmarriedunknownunknownnonotelephonemaymon21719990nonexistent1.193.994-36.44.8575191.0no
8824techniciansingleprofessional.coursenoyesnotelephonemaymon38019990nonexistent1.193.994-36.44.8575191.0no
9925servicessinglehigh.schoolnoyesnotelephonemaymon5019990nonexistent1.193.994-36.44.8575191.0no

Last rows

df_indexagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedoutput
411664117862retiredmarrieduniversity.degreenononocellularnovthu483263success-1.194.767-50.81.0314963.6yes
411674117964retireddivorcedprofessional.coursenoyesnocellularnovfri15139990nonexistent-1.194.767-50.81.0284963.6no
411684118036adminmarrieduniversity.degreenononocellularnovfri25429990nonexistent-1.194.767-50.81.0284963.6no
411694118137adminmarrieduniversity.degreenoyesnocellularnovfri28119990nonexistent-1.194.767-50.81.0284963.6yes
411704118229unemployedsinglebasic.4ynoyesnocellularnovfri112191success-1.194.767-50.81.0284963.6no
411714118373retiredmarriedprofessional.coursenoyesnocellularnovfri33419990nonexistent-1.194.767-50.81.0284963.6yes
411724118446blue-collarmarriedprofessional.coursenononocellularnovfri38319990nonexistent-1.194.767-50.81.0284963.6no
411734118556retiredmarrieduniversity.degreenoyesnocellularnovfri18929990nonexistent-1.194.767-50.81.0284963.6no
411744118644technicianmarriedprofessional.coursenononocellularnovfri44219990nonexistent-1.194.767-50.81.0284963.6yes
411754118774retiredmarriedprofessional.coursenoyesnocellularnovfri23939991failure-1.194.767-50.81.0284963.6no